Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations53940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory88.0 B

Variable types

Numeric8
Categorical3

Alerts

carat is highly overall correlated with price and 3 other fieldsHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2024-12-15 16:57:26.559353
Analysis finished2024-12-15 16:57:41.290266
Duration14.73 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct53940
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26970.5
Minimum1
Maximum53940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:41.506229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2697.95
Q113485.75
median26970.5
Q340455.25
95-th percentile51243.05
Maximum53940
Range53939
Interquartile range (IQR)26969.5

Descriptive statistics

Standard deviation15571.281
Coefficient of variation (CV)0.57734492
Kurtosis-1.2
Mean26970.5
Median Absolute Deviation (MAD)13485
Skewness0
Sum1.4547888 × 109
Variance2.424648 × 108
MonotonicityStrictly increasing
2024-12-15T18:57:41.750359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
35978 1
 
< 0.1%
35954 1
 
< 0.1%
35955 1
 
< 0.1%
35956 1
 
< 0.1%
35957 1
 
< 0.1%
35958 1
 
< 0.1%
35959 1
 
< 0.1%
35960 1
 
< 0.1%
35961 1
 
< 0.1%
Other values (53930) 53930
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
53940 1
< 0.1%
53939 1
< 0.1%
53938 1
< 0.1%
53937 1
< 0.1%
53936 1
< 0.1%
53935 1
< 0.1%
53934 1
< 0.1%
53933 1
< 0.1%
53932 1
< 0.1%
53931 1
< 0.1%

carat
Real number (ℝ)

High correlation 

Distinct273
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79793975
Minimum0.2
Maximum5.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:42.152375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.7
Q31.04
95-th percentile1.7
Maximum5.01
Range4.81
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.47401124
Coefficient of variation (CV)0.59404391
Kurtosis1.2566353
Mean0.79793975
Median Absolute Deviation (MAD)0.32
Skewness1.1166459
Sum43040.87
Variance0.22468666
MonotonicityNot monotonic
2024-12-15T18:57:42.570832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 2604
 
4.8%
0.31 2249
 
4.2%
1.01 2242
 
4.2%
0.7 1981
 
3.7%
0.32 1840
 
3.4%
1 1558
 
2.9%
0.9 1485
 
2.8%
0.41 1382
 
2.6%
0.4 1299
 
2.4%
0.71 1294
 
2.4%
Other values (263) 36006
66.8%
ValueCountFrequency (%)
0.2 12
 
< 0.1%
0.21 9
 
< 0.1%
0.22 5
 
< 0.1%
0.23 293
0.5%
0.24 254
0.5%
0.25 212
0.4%
0.26 253
0.5%
0.27 233
0.4%
0.28 198
0.4%
0.29 130
0.2%
ValueCountFrequency (%)
5.01 1
< 0.1%
4.5 1
< 0.1%
4.13 1
< 0.1%
4.01 2
< 0.1%
4 1
< 0.1%
3.67 1
< 0.1%
3.65 1
< 0.1%
3.51 1
< 0.1%
3.5 1
< 0.1%
3.4 1
< 0.1%

cut
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size421.5 KiB
Ideal
21551 
Premium
13791 
Very Good
12082 
Good
4906 
Fair
 
1610

Length

Max length9
Median length7
Mean length6.2865035
Min length4

Characters and Unicode

Total characters339094
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIdeal
2nd rowPremium
3rd rowGood
4th rowPremium
5th rowGood

Common Values

ValueCountFrequency (%)
Ideal 21551
40.0%
Premium 13791
25.6%
Very Good 12082
22.4%
Good 4906
 
9.1%
Fair 1610
 
3.0%

Length

2024-12-15T18:57:42.786848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T18:57:42.970874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ideal 21551
32.6%
good 16988
25.7%
premium 13791
20.9%
very 12082
18.3%
fair 1610
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 47424
14.0%
d 38539
11.4%
o 33976
10.0%
m 27582
8.1%
r 27483
8.1%
a 23161
 
6.8%
I 21551
 
6.4%
l 21551
 
6.4%
G 16988
 
5.0%
i 15401
 
4.5%
Other values (6) 65438
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 339094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 47424
14.0%
d 38539
11.4%
o 33976
10.0%
m 27582
8.1%
r 27483
8.1%
a 23161
 
6.8%
I 21551
 
6.4%
l 21551
 
6.4%
G 16988
 
5.0%
i 15401
 
4.5%
Other values (6) 65438
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 339094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 47424
14.0%
d 38539
11.4%
o 33976
10.0%
m 27582
8.1%
r 27483
8.1%
a 23161
 
6.8%
I 21551
 
6.4%
l 21551
 
6.4%
G 16988
 
5.0%
i 15401
 
4.5%
Other values (6) 65438
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 339094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 47424
14.0%
d 38539
11.4%
o 33976
10.0%
m 27582
8.1%
r 27483
8.1%
a 23161
 
6.8%
I 21551
 
6.4%
l 21551
 
6.4%
G 16988
 
5.0%
i 15401
 
4.5%
Other values (6) 65438
19.3%

color
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size421.5 KiB
G
11292 
E
9797 
F
9542 
H
8304 
D
6775 
Other values (2)
8230 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters53940
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowI
5th rowJ

Common Values

ValueCountFrequency (%)
G 11292
20.9%
E 9797
18.2%
F 9542
17.7%
H 8304
15.4%
D 6775
12.6%
I 5422
10.1%
J 2808
 
5.2%

Length

2024-12-15T18:57:43.156880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T18:57:43.316875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
g 11292
20.9%
e 9797
18.2%
f 9542
17.7%
h 8304
15.4%
d 6775
12.6%
i 5422
10.1%
j 2808
 
5.2%

Most occurring characters

ValueCountFrequency (%)
G 11292
20.9%
E 9797
18.2%
F 9542
17.7%
H 8304
15.4%
D 6775
12.6%
I 5422
10.1%
J 2808
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 11292
20.9%
E 9797
18.2%
F 9542
17.7%
H 8304
15.4%
D 6775
12.6%
I 5422
10.1%
J 2808
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 11292
20.9%
E 9797
18.2%
F 9542
17.7%
H 8304
15.4%
D 6775
12.6%
I 5422
10.1%
J 2808
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 11292
20.9%
E 9797
18.2%
F 9542
17.7%
H 8304
15.4%
D 6775
12.6%
I 5422
10.1%
J 2808
 
5.2%

clarity
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size421.5 KiB
SI1
13065 
VS2
12258 
SI2
9194 
VS1
8171 
VVS2
5066 
Other values (3)
6186 

Length

Max length4
Median length3
Mean length3.1147571
Min length2

Characters and Unicode

Total characters168010
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI2
2nd rowSI1
3rd rowVS1
4th rowVS2
5th rowSI2

Common Values

ValueCountFrequency (%)
SI1 13065
24.2%
VS2 12258
22.7%
SI2 9194
17.0%
VS1 8171
15.1%
VVS2 5066
 
9.4%
VVS1 3655
 
6.8%
IF 1790
 
3.3%
I1 741
 
1.4%

Length

2024-12-15T18:57:43.536874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T18:57:43.730846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si1 13065
24.2%
vs2 12258
22.7%
si2 9194
17.0%
vs1 8171
15.1%
vvs2 5066
 
9.4%
vvs1 3655
 
6.8%
if 1790
 
3.3%
i1 741
 
1.4%

Most occurring characters

ValueCountFrequency (%)
S 51409
30.6%
V 37871
22.5%
2 26518
15.8%
1 25632
15.3%
I 24790
14.8%
F 1790
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 51409
30.6%
V 37871
22.5%
2 26518
15.8%
1 25632
15.3%
I 24790
14.8%
F 1790
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 51409
30.6%
V 37871
22.5%
2 26518
15.8%
1 25632
15.3%
I 24790
14.8%
F 1790
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 51409
30.6%
V 37871
22.5%
2 26518
15.8%
1 25632
15.3%
I 24790
14.8%
F 1790
 
1.1%

depth
Real number (ℝ)

Distinct184
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.749405
Minimum43
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:43.943841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile59.3
Q161
median61.8
Q362.5
95-th percentile63.8
Maximum79
Range36
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4326213
Coefficient of variation (CV)0.023200569
Kurtosis5.7394146
Mean61.749405
Median Absolute Deviation (MAD)0.7
Skewness-0.082294026
Sum3330762.9
Variance2.0524038
MonotonicityNot monotonic
2024-12-15T18:57:44.171606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 2239
 
4.2%
61.9 2163
 
4.0%
61.8 2077
 
3.9%
62.2 2039
 
3.8%
62.1 2020
 
3.7%
61.6 1956
 
3.6%
62.3 1940
 
3.6%
61.7 1904
 
3.5%
62.4 1792
 
3.3%
61.5 1719
 
3.2%
Other values (174) 34091
63.2%
ValueCountFrequency (%)
43 2
< 0.1%
44 1
< 0.1%
50.8 1
< 0.1%
51 1
< 0.1%
52.2 1
< 0.1%
52.3 1
< 0.1%
52.7 1
< 0.1%
53 1
< 0.1%
53.1 1
< 0.1%
53.2 2
< 0.1%
ValueCountFrequency (%)
79 2
< 0.1%
78.2 1
< 0.1%
73.6 1
< 0.1%
72.9 1
< 0.1%
72.2 1
< 0.1%
71.8 1
< 0.1%
71.6 2
< 0.1%
71.3 1
< 0.1%
71.2 1
< 0.1%
71 1
< 0.1%

table
Real number (ℝ)

Distinct127
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.457184
Minimum43
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:44.376606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q156
median57
Q359
95-th percentile61
Maximum95
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2344906
Coefficient of variation (CV)0.038889664
Kurtosis2.8018569
Mean57.457184
Median Absolute Deviation (MAD)1
Skewness0.79689585
Sum3099240.5
Variance4.9929481
MonotonicityNot monotonic
2024-12-15T18:57:44.612241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 9881
18.3%
57 9724
18.0%
58 8369
15.5%
59 6572
12.2%
55 6268
11.6%
60 4241
7.9%
54 2594
 
4.8%
61 2282
 
4.2%
62 1273
 
2.4%
63 588
 
1.1%
Other values (117) 2148
 
4.0%
ValueCountFrequency (%)
43 1
 
< 0.1%
44 1
 
< 0.1%
49 2
 
< 0.1%
50 2
 
< 0.1%
50.1 1
 
< 0.1%
51 9
 
< 0.1%
51.6 1
 
< 0.1%
52 56
0.1%
52.4 1
 
< 0.1%
52.8 2
 
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
79 1
 
< 0.1%
76 1
 
< 0.1%
73 4
 
< 0.1%
71 1
 
< 0.1%
70 9
 
< 0.1%
69 9
 
< 0.1%
68 21
 
< 0.1%
67 42
0.1%
66 91
0.2%

price
Real number (ℝ)

High correlation 

Distinct11602
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3932.7997
Minimum326
Maximum18823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:44.920661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile544
Q1950
median2401
Q35324.25
95-th percentile13107.1
Maximum18823
Range18497
Interquartile range (IQR)4374.25

Descriptive statistics

Standard deviation3989.4397
Coefficient of variation (CV)1.014402
Kurtosis2.1776958
Mean3932.7997
Median Absolute Deviation (MAD)1670
Skewness1.6183953
Sum2.1213522 × 108
Variance15915629
MonotonicityNot monotonic
2024-12-15T18:57:45.147770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605 132
 
0.2%
802 127
 
0.2%
625 126
 
0.2%
828 125
 
0.2%
776 124
 
0.2%
698 121
 
0.2%
789 121
 
0.2%
544 120
 
0.2%
666 114
 
0.2%
552 113
 
0.2%
Other values (11592) 52717
97.7%
ValueCountFrequency (%)
326 2
< 0.1%
327 1
< 0.1%
334 1
< 0.1%
335 1
< 0.1%
336 2
< 0.1%
337 2
< 0.1%
338 1
< 0.1%
339 1
< 0.1%
340 1
< 0.1%
342 1
< 0.1%
ValueCountFrequency (%)
18823 1
< 0.1%
18818 1
< 0.1%
18806 1
< 0.1%
18804 1
< 0.1%
18803 1
< 0.1%
18797 1
< 0.1%
18795 2
< 0.1%
18791 2
< 0.1%
18788 1
< 0.1%
18787 1
< 0.1%

x
Real number (ℝ)

High correlation 

Distinct554
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7311572
Minimum0
Maximum10.74
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:45.377735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.71
median5.7
Q36.54
95-th percentile7.66
Maximum10.74
Range10.74
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.1217607
Coefficient of variation (CV)0.19573023
Kurtosis-0.61816067
Mean5.7311572
Median Absolute Deviation (MAD)0.93
Skewness0.37867634
Sum309138.62
Variance1.2583472
MonotonicityNot monotonic
2024-12-15T18:57:45.595768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37 448
 
0.8%
4.34 437
 
0.8%
4.33 429
 
0.8%
4.38 428
 
0.8%
4.32 425
 
0.8%
4.35 407
 
0.8%
4.39 388
 
0.7%
4.31 387
 
0.7%
4.36 386
 
0.7%
4.4 373
 
0.7%
Other values (544) 49832
92.4%
ValueCountFrequency (%)
0 8
< 0.1%
3.73 2
 
< 0.1%
3.74 1
 
< 0.1%
3.76 1
 
< 0.1%
3.77 1
 
< 0.1%
3.79 2
 
< 0.1%
3.81 3
 
< 0.1%
3.82 2
 
< 0.1%
3.83 3
 
< 0.1%
3.84 4
< 0.1%
ValueCountFrequency (%)
10.74 1
< 0.1%
10.23 1
< 0.1%
10.14 1
< 0.1%
10.02 1
< 0.1%
10.01 1
< 0.1%
10 1
< 0.1%
9.86 1
< 0.1%
9.66 1
< 0.1%
9.65 1
< 0.1%
9.54 1
< 0.1%

y
Real number (ℝ)

High correlation 

Distinct552
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.734526
Minimum0
Maximum58.9
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:45.792715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.3
Q14.72
median5.71
Q36.54
95-th percentile7.65
Maximum58.9
Range58.9
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.1421347
Coefficient of variation (CV)0.19916811
Kurtosis91.214557
Mean5.734526
Median Absolute Deviation (MAD)0.92
Skewness2.4341667
Sum309320.33
Variance1.3044716
MonotonicityNot monotonic
2024-12-15T18:57:46.004712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.34 437
 
0.8%
4.37 435
 
0.8%
4.35 425
 
0.8%
4.33 421
 
0.8%
4.32 414
 
0.8%
4.39 407
 
0.8%
4.38 406
 
0.8%
4.4 387
 
0.7%
4.31 386
 
0.7%
4.41 384
 
0.7%
Other values (542) 49838
92.4%
ValueCountFrequency (%)
0 7
< 0.1%
3.68 1
 
< 0.1%
3.71 2
 
< 0.1%
3.72 1
 
< 0.1%
3.73 1
 
< 0.1%
3.75 1
 
< 0.1%
3.77 2
 
< 0.1%
3.78 5
< 0.1%
3.8 1
 
< 0.1%
3.81 1
 
< 0.1%
ValueCountFrequency (%)
58.9 1
< 0.1%
31.8 1
< 0.1%
10.54 1
< 0.1%
10.16 1
< 0.1%
10.1 1
< 0.1%
9.94 2
< 0.1%
9.85 1
< 0.1%
9.81 1
< 0.1%
9.63 1
< 0.1%
9.59 1
< 0.1%

z
Real number (ℝ)

High correlation 

Distinct375
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5387338
Minimum0
Maximum31.8
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size421.5 KiB
2024-12-15T18:57:46.204757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.65
Q12.91
median3.53
Q34.04
95-th percentile4.73
Maximum31.8
Range31.8
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.70569885
Coefficient of variation (CV)0.19942129
Kurtosis47.086619
Mean3.5387338
Median Absolute Deviation (MAD)0.57
Skewness1.5224226
Sum190879.3
Variance0.49801086
MonotonicityNot monotonic
2024-12-15T18:57:46.416680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 767
 
1.4%
2.69 748
 
1.4%
2.71 738
 
1.4%
2.68 730
 
1.4%
2.72 697
 
1.3%
2.67 649
 
1.2%
2.73 612
 
1.1%
2.66 555
 
1.0%
2.74 548
 
1.0%
4.02 538
 
1.0%
Other values (365) 47358
87.8%
ValueCountFrequency (%)
0 20
< 0.1%
1.07 1
 
< 0.1%
1.41 1
 
< 0.1%
1.53 1
 
< 0.1%
2.06 1
 
< 0.1%
2.24 1
 
< 0.1%
2.25 1
 
< 0.1%
2.26 1
 
< 0.1%
2.27 1
 
< 0.1%
2.28 1
 
< 0.1%
ValueCountFrequency (%)
31.8 1
< 0.1%
8.06 1
< 0.1%
6.98 1
< 0.1%
6.72 1
< 0.1%
6.43 1
< 0.1%
6.38 1
< 0.1%
6.31 1
< 0.1%
6.27 1
< 0.1%
6.24 1
< 0.1%
6.17 1
< 0.1%

Interactions

2024-12-15T18:57:39.115225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:28.286758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:30.353322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:31.831914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:33.512730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:34.769514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:36.297752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:37.558773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:39.257468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:28.524324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:30.592839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:32.001862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:33.672685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:34.981548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:36.439798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:37.741894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:39.393469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:28.750368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:30.769634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:32.166910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:33.811727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:35.178499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:36.579486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:37.896173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:39.560134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:28.985326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:30.934663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:32.384908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:33.979729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:35.440345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:36.731439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:38.049217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:39.800138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:29.288321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:31.124646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:32.529910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:34.120696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:35.594378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:36.870449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:38.185260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:40.096429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:29.569323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:31.350667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:32.707142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:34.293730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:35.784344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:37.021208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:38.529213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:40.245613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:29.828328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:31.515632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:33.138687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:34.441740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:35.935360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:37.203166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:38.755215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:40.406570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:30.095329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:31.673131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:33.301726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:34.594501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:36.141798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:37.361166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T18:57:38.945211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-15T18:57:46.572633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Unnamed: 0caratclaritycolorcutdepthpricetablexyz
Unnamed: 01.000-0.4160.1550.0900.099-0.047-0.404-0.114-0.415-0.417-0.415
carat-0.4161.0000.1590.1350.1160.0300.9630.1950.9960.9960.993
clarity0.1550.1591.0000.0790.1420.0770.1450.0500.1580.1980.201
color0.0900.1350.0791.0000.0360.0210.0930.0210.1300.1330.100
cut0.0990.1160.1420.0361.0000.4060.0930.2900.1480.1080.096
depth-0.0470.0300.0770.0210.4061.0000.010-0.245-0.023-0.0250.103
price-0.4040.9630.1450.0930.0930.0101.0000.1720.9630.9630.957
table-0.1140.1950.0500.0210.290-0.2450.1721.0000.2020.1960.160
x-0.4150.9960.1580.1300.148-0.0230.9630.2021.0000.9980.987
y-0.4170.9960.1980.1330.108-0.0250.9630.1960.9981.0000.987
z-0.4150.9930.2010.1000.0960.1030.9570.1600.9870.9871.000

Missing values

2024-12-15T18:57:40.702585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-15T18:57:41.005648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0caratcutcolorclaritydepthtablepricexyz
010.23IdealESI261.555.03263.953.982.43
120.21PremiumESI159.861.03263.893.842.31
230.23GoodEVS156.965.03274.054.072.31
340.29PremiumIVS262.458.03344.204.232.63
450.31GoodJSI263.358.03354.344.352.75
560.24Very GoodJVVS262.857.03363.943.962.48
670.24Very GoodIVVS162.357.03363.953.982.47
780.26Very GoodHSI161.955.03374.074.112.53
890.22FairEVS265.161.03373.873.782.49
9100.23Very GoodHVS159.461.03384.004.052.39
Unnamed: 0caratcutcolorclaritydepthtablepricexyz
53930539310.71PremiumESI160.555.027565.795.743.49
53931539320.71PremiumFSI159.862.027565.745.733.43
53932539330.70Very GoodEVS260.559.027575.715.763.47
53933539340.70Very GoodEVS261.259.027575.695.723.49
53934539350.72PremiumDSI162.759.027575.695.733.58
53935539360.72IdealDSI160.857.027575.755.763.50
53936539370.72GoodDSI163.155.027575.695.753.61
53937539380.70Very GoodDSI162.860.027575.665.683.56
53938539390.86PremiumHSI261.058.027576.156.123.74
53939539400.75IdealDSI262.255.027575.835.873.64